Miners, firefighters, and soldiers often operate in contained environments under danger of injury. The ability to locate an injured individual in a contained environment with accuracy speeds rescue efforts and therefore survival rates. Unfortunately, contained environments associated with mines, buildings, and remote battlefields render absolute position techniques such as global positioning satellite (GPS) based systems and triangulation methods ineffective due to signal interference caused by surrounding structures.
This problem, commonly referred to as pedestrian dead-reckoning (PDR), has been investigated and attempts to provide a working solution have involved various types of sensors such as acceleration sensors (based on acceleration) and gyroscopic sensors (based on rotational velocity), magnetic sensors (based on gyroscope heading), pressure sensors (based on altitude), ultrasonic sensors (based on distance), and cameras (based on stereo vision). Both acceleration sensors and gyroscopic sensors are subject to noise and bias. In general, there are three sources of error in measurements [10]: noise because of sensory electronics, bias of the sensor, and unknown scaling factor and offset of the sensor. Other sensor types also have distance limitations or are prone to distortions associated with a containing environment or clock timing limitations. Efforts to account for these errors have met with limited success and as a result a satisfactory subject tracking system within confined environments has not been developed.
The sensor in PDR systems is routinely placed on the foot of a user. The foot is usually the part of the body with the highest motion amplitude, and placing the sensor on the foot is considered to provide the highest signal-to-noise ratio of the sensor outputs. The foot also has the beneficial attribute of acting as a pivot point during walking motions and goes through a stationary period during the cyclic act of stepping. Thus, the sensor output is zero during the stationary period and allowing for zero-velocity updates, which can be used to correct estimation errors. Head-mounted and waist-mounted sensors have also been considered [2,5,12,13]. Ojeda et al. [17] have shown that the front of the sole is the part of the shoe that remains stationary for the longest period of time and is therefore the most suitable sensor position for zero velocity error corrections.
Noise is caused by the electronics that are needed for reading the correct sensor output. This noise is often characterized as zero-mean white Gaussian noise and often expressed in signal/√{square root over (Hz)}. The bias of the sensors can be split up in two parts. The first one is the turn-on bias which is different every time the device is powered on. The other is the in-run bias, which varies with motion. Both of these biases are dependent on temperature. This makes a total bias of:btotal=bturn-on+bin-run The scaling factor and the offset are modeled as intrinsic sensor properties which do not change because of external factors, but are different for every sensor.
There are generally two categories of PDR systems described in literature. The first is based on step-counting and combines sensor signal-features like frequency, variance and mean value to estimate the stride. The second category (classical pedestrian navigation) is based on double integration of the acceleration signals to determine position.
The general layout of the most basic classical pedestrian navigation system is shown in prior art FIG. 1. The main sensors in such a system are accelerometers and angular rate sensors (gyroscopes). The 3D accelerometer provides the system with the magnitude of movements.
Because the accelerometer moves with the body, it is rotated with respect to the world frame. This makes it necessary to measure this rotation. This is done using the gyroscopes which measure the angular rate. Because of the fact that rotation operations in 3D are not commutative, the rotation cannot be found by integrating every angular rate component independently. Several methods exist to describe rotations.
A major problem in classical navigation is caused by low-frequency errors in the accelerometer and gyroscope signals because of bias and noise. As both the gyroscope and accelerometer signals are integrated, bias in the sensors will cause increasing errors in attitude and velocity, which will propagate into the position estimates. These errors have been dealt with through a zero-velocity update (often abbreviated as ZUPT or ZVU). Zero-velocity updates incorporate the estimation of bias and velocity errors when the sensors are stationary for a certain period of time. As the motions found during human movements are rather complex, implementations of the zero-velocity update assume that periods of zero acceleration are also periods of zero velocity (this is not true for motions in general). This assumption makes it possible to detect these periods by checking if the magnitudes of the acceleration and the angular rates are under a certain threshold [11]. Measurements done using zero-velocity updates are generally noisy as the foot is never completely still and the user position accuracy is limited. A common way to estimate sensor bias is using a complementary Kalman filter [3,5,7,11,12,14]. This filter is optimal for Gaussian measurement noise and Gaussian system noise, which is applicable in such measurements. However, because the measurements require non-linear transformations (rotation) to fit the state variables, the extended Kalman filter is mainly used.
The second method of stride estimation is using a regression model on several signal features. This method does not rely on the double integration of the acceleration and suffers less from bias errors. Instead, step frequency and acceleration signal properties like variance, mean and amplitude are used for error estimation. These parameters are in general determined from the acceleration magnitude instead of the individual acceleration signals. This is done to remove the influence of gravity due to a change in orientation:|a|=√{square root over (ax2+ay2+az2)}−g, where g is the gravity
The parameters are combined in a regression model, which matches these partly independent parameters to an estimate for the travelled distance. The used regression models are often linear [10,13,14] but experiments with function approximators (neural networks) have proven to be successful [2,15]. Because the same parameters can be found for numerous motions (forward walk, backward walk, walk on the spot), this method of pedestrian dead-reckoning is very motion dependant. In most of these models, a forward walk on flat ground is assumed with a natural pattern. Because first responders generally make all kinds of movements, this would require an accurate motion classification.
The primary form of motion classification used in most of the literature [3,13,14] is step detection. To detect steps, features of the sensor information need to be found that uniquely define a step and this has proven difficult.
Particle filtering is an approximation of Bayesian estimation, using an approximation of the probability density functions by a discrete distribution of weighted particles that is also used for error estimation. The strength of particle filters in pedestrian dead-reckoning is that it can cope with non-linear probability functions and can therefore cope with certain rules about which particles are possible solutions and which particles are not. This technique has been used by Widyawan et al. [1] and Woodman et al. [6] to develop an indoor positioning system, complemented by a building plan.
In spite of these efforts and ever-increasing complexity of signal processing there exists a need for a system and a process for reliably locating a person in a contained environment even when the person goes through times of kneeling, climbing, and other activities that have proven difficult to model and therefore distort the calculated position of the user. There also exists a need for a subject tracking system that provides positioning between a subject and a transceiver without reliance on external GPS interaction or clock synchronization therebetween that have confounded prior art distance separation tracking processes.